# AnalyticBridge

A Data Science Central Community

Written by Claudio Lucio do Val Lopes. See also his video illustrating the concept. Based on the First Analyticbridge Theorem.

set.seed(3.1416)
x <- rnorm(1000000,10,2)

analytic_teorem <- function (N,x,med,desv)

{
length(x)
meansamples <- rep(NA,N)
for(i in 1:N){
meansamples[i]  <- mean(sample(x, length(x)/N, replace = TRUE))
}
meansamples <- sort(meansamples)
confidence <- rep(NA,N/2)
p_lower <- rep(NA,N/2)
p_upper <- rep(NA,N/2)

for(k in 1:N/2){
confidence[k] <- 2*k/(N+1)
p_lower[k] <- meansamples[k]
p_upper[k] <- meansamples[N-k+1]
}

mean_t <- (meansamples[k] + meansamples[N-k+1])/2

x_a <- seq(1:(N/2))
par(mar=c(5,4,4,5)+.1)

plot(x_a, p_upper,type="l", ylim=range(c(med-1*desv,med+1*

desv)),col="blue",xlab="K",ylab="Values")

lines(x_a, p_lower,type="l",col="red",xaxt="n",yaxt="n",xlab="",ylab="")

par(new=TRUE)

plot(confidence*(N/2),rep(mean_t,N/2),ylim=range(c(med-1*desv,med+1*desv)),type="l",

col="black",xaxt="n",yaxt="n",ylab='',xlab='',lty=2)
axis(3,seq(from=0,to=1,by=.25)*(N/2),las=0,at=seq(from=0,to=1,by=.25)*(N/2),labels=c(1,0.75,0.50,0.25,0))
mtext("Confidence",side=3,line=3)
legend("bottomright",col=c("red","blue"),lty=1,legend=c("Lower bound","Upper Bound"))
legend("topright",legend=c(paste("N=",as.character(N))))
}

for(i in seq(1000,10000,100)) analytic_teorem(i,x,10,2)

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